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Different machine learning approaches on classifying customers who are most likely to purchase an offer. Made with Jupyter Notebook, scikit-learn, and other helpful python packages.

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KyleZrey/membership-classification

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README.md
README.md
 
 
clean.parquet
clean.parquet
 
 
decision-trees.ipynb
decision-trees.ipynb
 
 
kNN.ipynb
kNN.ipynb
 
 
linear.ipynb
linear.ipynb
 
 
logistic.ipynb
logistic.ipynb
 
 
logpoly.ipynb
logpoly.ipynb
 
 
marketing_data.xlsx
marketing_data.xlsx
 
 
polynomial.ipynb
polynomial.ipynb
 
 
preprocessing.ipynb
preprocessing.ipynb
 
 
svm.ipynb
svm.ipynb
 
 
test.ipynb
test.ipynb
 
 

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membership-classification

Different approaches on classifying customers who are most likely to purchase an offer. The said classification approaches are: Support Vector Machines (SVM), k-Nearest Neighbors (kNN), Decision Tree (DT), Logistic with PolynomialFeatures, Logistic, Linear with Polynomial Features, and Linear Classification. Made with Jupyter Notebook, scikit-learn, and other helpful python packages.

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Different machine learning approaches on classifying customers who are most likely to purchase an offer. Made with Jupyter Notebook, scikit-learn, and other helpful python packages.

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machine-learning artificial-intelligence decision-trees support-vector-machines k-nearest-neighbors linear-classification logistic-classification

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